From 40ec71da81616151f199b9be52dcf2d061e17b37 Mon Sep 17 00:00:00 2001 From: James Betker Date: Mon, 4 Jan 2021 10:54:34 -0700 Subject: [PATCH] Move styled_sr into its own folder --- codes/models/styled_sr/__init__.py | 0 .../{improve_rrdb => styled_sr}/styled_sr.py | 0 codes/models/styled_sr/stylegan2_base.py | 964 ++++++++++++++++++ 3 files changed, 964 insertions(+) create mode 100644 codes/models/styled_sr/__init__.py rename codes/models/{improve_rrdb => styled_sr}/styled_sr.py (100%) create mode 100644 codes/models/styled_sr/stylegan2_base.py diff --git a/codes/models/styled_sr/__init__.py b/codes/models/styled_sr/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/codes/models/improve_rrdb/styled_sr.py b/codes/models/styled_sr/styled_sr.py similarity index 100% rename from codes/models/improve_rrdb/styled_sr.py rename to codes/models/styled_sr/styled_sr.py diff --git a/codes/models/styled_sr/stylegan2_base.py b/codes/models/styled_sr/stylegan2_base.py new file mode 100644 index 00000000..06517add --- /dev/null +++ b/codes/models/styled_sr/stylegan2_base.py @@ -0,0 +1,964 @@ +import functools +import math +import multiprocessing +from contextlib import contextmanager, ExitStack +from functools import partial +from math import log2 +from random import random + +import torch +import torch.nn.functional as F +import trainer.losses as L +import numpy as np + +from kornia.filters import filter2D +from linear_attention_transformer import ImageLinearAttention +from torch import nn +from torch.autograd import grad as torch_grad +from vector_quantize_pytorch import VectorQuantize + +from trainer.networks import register_model +from utils.util import checkpoint, opt_get + +try: + from apex import amp + + APEX_AVAILABLE = True +except: + APEX_AVAILABLE = False + +assert torch.cuda.is_available(), 'You need to have an Nvidia GPU with CUDA installed.' + +num_cores = multiprocessing.cpu_count() + +# constants + +EPS = 1e-8 +CALC_FID_NUM_IMAGES = 12800 + + +# helper classes + +def DiffAugment(x, types=[]): + for p in types: + for f in AUGMENT_FNS[p]: + x = f(x) + return x.contiguous() + +def rand_brightness(x): + x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5) + return x + +def rand_saturation(x): + x_mean = x.mean(dim=1, keepdim=True) + x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean + return x + +def rand_contrast(x): + x_mean = x.mean(dim=[1, 2, 3], keepdim=True) + x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean + return x + +def rand_translation(x, ratio=0.125): + shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) + translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device) + translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device) + grid_batch, grid_x, grid_y = torch.meshgrid( + torch.arange(x.size(0), dtype=torch.long, device=x.device), + torch.arange(x.size(2), dtype=torch.long, device=x.device), + torch.arange(x.size(3), dtype=torch.long, device=x.device), + ) + grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1) + grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1) + x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0]) + x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2) + return x + +def rand_cutout(x, ratio=0.5): + cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5) + offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device) + offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device) + grid_batch, grid_x, grid_y = torch.meshgrid( + torch.arange(x.size(0), dtype=torch.long, device=x.device), + torch.arange(cutout_size[0], dtype=torch.long, device=x.device), + torch.arange(cutout_size[1], dtype=torch.long, device=x.device), + ) + grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1) + grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1) + mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device) + mask[grid_batch, grid_x, grid_y] = 0 + x = x * mask.unsqueeze(1) + return x + +AUGMENT_FNS = { + 'color': [rand_brightness, rand_saturation, rand_contrast], + 'translation': [rand_translation], + 'cutout': [rand_cutout], +} + +class NanException(Exception): + pass + + +class EMA(): + def __init__(self, beta): + super().__init__() + self.beta = beta + + def update_average(self, old, new): + if not exists(old): + return new + return old * self.beta + (1 - self.beta) * new + + +class Flatten(nn.Module): + def forward(self, x): + return x.reshape(x.shape[0], -1) + + +class Residual(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + + def forward(self, x): + return self.fn(x) + x + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x): + return self.fn(x) * self.g + + +class PermuteToFrom(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + + def forward(self, x): + x = x.permute(0, 2, 3, 1) + out, loss = self.fn(x) + out = out.permute(0, 3, 1, 2) + return out, loss + + +class Blur(nn.Module): + def __init__(self): + super().__init__() + f = torch.Tensor([1, 2, 1]) + self.register_buffer('f', f) + + def forward(self, x): + f = self.f + f = f[None, None, :] * f[None, :, None] + return filter2D(x, f, normalized=True) + + +# one layer of self-attention and feedforward, for images + +attn_and_ff = lambda chan: nn.Sequential(*[ + Residual(Rezero(ImageLinearAttention(chan, norm_queries=True))), + Residual(Rezero(nn.Sequential(nn.Conv2d(chan, chan * 2, 1), leaky_relu(), nn.Conv2d(chan * 2, chan, 1)))) +]) + + +# helpers + +def exists(val): + return val is not None + + +@contextmanager +def null_context(): + yield + + +def combine_contexts(contexts): + @contextmanager + def multi_contexts(): + with ExitStack() as stack: + yield [stack.enter_context(ctx()) for ctx in contexts] + + return multi_contexts + + +def default(value, d): + return value if exists(value) else d + + +def cycle(iterable): + while True: + for i in iterable: + yield i + + +def cast_list(el): + return el if isinstance(el, list) else [el] + + +def is_empty(t): + if isinstance(t, torch.Tensor): + return t.nelement() == 0 + return not exists(t) + + +def raise_if_nan(t): + if torch.isnan(t): + raise NanException + + +def gradient_accumulate_contexts(gradient_accumulate_every, is_ddp, ddps): + if is_ddp: + num_no_syncs = gradient_accumulate_every - 1 + head = [combine_contexts(map(lambda ddp: ddp.no_sync, ddps))] * num_no_syncs + tail = [null_context] + contexts = head + tail + else: + contexts = [null_context] * gradient_accumulate_every + + for context in contexts: + with context(): + yield + + +def loss_backwards(fp16, loss, optimizer, loss_id, **kwargs): + if fp16: + with amp.scale_loss(loss, optimizer, loss_id) as scaled_loss: + scaled_loss.backward(**kwargs) + else: + loss.backward(**kwargs) + + +def gradient_penalty(images, output, weight=10, return_structured_grads=False): + batch_size = images.shape[0] + gradients = torch_grad(outputs=output, inputs=images, + grad_outputs=torch.ones(output.size(), device=images.device), + create_graph=True, retain_graph=True, only_inputs=True)[0] + + flat_grad = gradients.reshape(batch_size, -1) + penalty = weight * ((flat_grad.norm(2, dim=1) - 1) ** 2).mean() + if return_structured_grads: + return penalty, gradients + else: + return penalty + +def calc_pl_lengths(styles, images): + device = images.device + num_pixels = images.shape[2] * images.shape[3] + pl_noise = torch.randn(images.shape, device=device) / math.sqrt(num_pixels) + outputs = (images * pl_noise).sum() + + pl_grads = torch_grad(outputs=outputs, inputs=styles, + grad_outputs=torch.ones(outputs.shape, device=device), + create_graph=True, retain_graph=True, only_inputs=True)[0] + + return (pl_grads ** 2).sum(dim=2).mean(dim=1).sqrt() + + +def image_noise(n, im_size, device): + return torch.FloatTensor(n, im_size, im_size, 1).uniform_(0., 1.).cuda(device) + + +def leaky_relu(p=0.2): + return nn.LeakyReLU(p, inplace=True) + + +def evaluate_in_chunks(max_batch_size, model, *args): + split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args)))) + chunked_outputs = [model(*i) for i in split_args] + if len(chunked_outputs) == 1: + return chunked_outputs[0] + return torch.cat(chunked_outputs, dim=0) + + +def set_requires_grad(model, bool): + for p in model.parameters(): + p.requires_grad = bool + + +def slerp(val, low, high): + low_norm = low / torch.norm(low, dim=1, keepdim=True) + high_norm = high / torch.norm(high, dim=1, keepdim=True) + omega = torch.acos((low_norm * high_norm).sum(1)) + so = torch.sin(omega) + res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high + return res + +# augmentations + +def random_hflip(tensor, prob): + if prob > random(): + return tensor + return torch.flip(tensor, dims=(3,)) + + +class StyleGan2Augmentor(nn.Module): + def __init__(self, D, image_size, types, prob): + super().__init__() + self.D = D + self.prob = prob + self.types = types + + def forward(self, images, detach=False): + if random() < self.prob: + images = random_hflip(images, prob=0.5) + images = DiffAugment(images, types=self.types) + + if detach: + images = images.detach() + + # Save away for use elsewhere (e.g. unet loss) + self.aug_images = images + + return self.D(images) + + def network_loaded(self): + self.D.network_loaded() + + +# stylegan2 classes + +class EqualLinear(nn.Module): + def __init__(self, in_dim, out_dim, lr_mul=1, bias=True): + super().__init__() + self.weight = nn.Parameter(torch.randn(out_dim, in_dim)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_dim)) + + self.lr_mul = lr_mul + + def forward(self, input): + return F.linear(input, self.weight * self.lr_mul, bias=self.bias * self.lr_mul) + + +class StyleVectorizer(nn.Module): + def __init__(self, emb, depth, lr_mul=0.1): + super().__init__() + + layers = [] + for i in range(depth): + layers.extend([EqualLinear(emb, emb, lr_mul), leaky_relu()]) + + self.net = nn.Sequential(*layers) + + def forward(self, x): + x = F.normalize(x, dim=1) + return self.net(x) + + +class RGBBlock(nn.Module): + def __init__(self, latent_dim, input_channel, upsample, rgba=False): + super().__init__() + self.input_channel = input_channel + self.to_style = nn.Linear(latent_dim, input_channel) + + out_filters = 3 if not rgba else 4 + self.conv = Conv2DMod(input_channel, out_filters, 1, demod=False) + + self.upsample = nn.Sequential( + nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False), + Blur() + ) if upsample else None + + def forward(self, x, prev_rgb, istyle): + b, c, h, w = x.shape + style = self.to_style(istyle) + x = self.conv(x, style) + + if exists(prev_rgb): + x = x + prev_rgb + + if exists(self.upsample): + x = self.upsample(x) + + return x + + +class AdaptiveInstanceNorm(nn.Module): + def __init__(self, in_channel, style_dim): + super().__init__() + from models.archs.arch_util import ConvGnLelu + self.style2scale = ConvGnLelu(style_dim, in_channel, kernel_size=1, norm=False, activation=False, bias=True) + self.style2bias = ConvGnLelu(style_dim, in_channel, kernel_size=1, norm=False, activation=False, bias=True, weight_init_factor=0) + self.norm = nn.InstanceNorm2d(in_channel) + + def forward(self, input, style): + gamma = self.style2scale(style) + beta = self.style2bias(style) + out = self.norm(input) + out = gamma * out + beta + return out + + +class NoiseInjection(nn.Module): + def __init__(self, channel): + super().__init__() + self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1)) + + def forward(self, image, noise): + return image + self.weight * noise + + +class EqualLR: + def __init__(self, name): + self.name = name + + def compute_weight(self, module): + weight = getattr(module, self.name + '_orig') + fan_in = weight.data.size(1) * weight.data[0][0].numel() + + return weight * math.sqrt(2 / fan_in) + + @staticmethod + def apply(module, name): + fn = EqualLR(name) + + weight = getattr(module, name) + del module._parameters[name] + module.register_parameter(name + '_orig', nn.Parameter(weight.data)) + module.register_forward_pre_hook(fn) + + return fn + + def __call__(self, module, input): + weight = self.compute_weight(module) + setattr(module, self.name, weight) + + +def equal_lr(module, name='weight'): + EqualLR.apply(module, name) + + return module + + +class EqualConv2d(nn.Module): + def __init__(self, *args, **kwargs): + super().__init__() + + conv = nn.Conv2d(*args, **kwargs) + conv.weight.data.normal_() + conv.bias.data.zero_() + self.conv = equal_lr(conv) + + def forward(self, input): + return self.conv(input) + + +class Conv2DMod(nn.Module): + def __init__(self, in_chan, out_chan, kernel, demod=True, stride=1, dilation=1, **kwargs): + super().__init__() + self.filters = out_chan + self.demod = demod + self.kernel = kernel + self.stride = stride + self.dilation = dilation + self.weight = nn.Parameter(torch.randn((out_chan, in_chan, kernel, kernel))) + nn.init.kaiming_normal_(self.weight, a=0, mode='fan_in', nonlinearity='leaky_relu') + + def _get_same_padding(self, size, kernel, dilation, stride): + return ((size - 1) * (stride - 1) + dilation * (kernel - 1)) // 2 + + def forward(self, x, y): + b, c, h, w = x.shape + + w1 = y[:, None, :, None, None] + w2 = self.weight[None, :, :, :, :] + weights = w2 * (w1 + 1) + + if self.demod: + d = torch.rsqrt((weights ** 2).sum(dim=(2, 3, 4), keepdim=True) + EPS) + weights = weights * d + + x = x.reshape(1, -1, h, w) + + _, _, *ws = weights.shape + weights = weights.reshape(b * self.filters, *ws) + + padding = self._get_same_padding(h, self.kernel, self.dilation, self.stride) + x = F.conv2d(x, weights, padding=padding, groups=b) + + x = x.reshape(-1, self.filters, h, w) + return x + + +class GeneratorBlockWithStructure(nn.Module): + def __init__(self, latent_dim, input_channels, filters, upsample=True, upsample_rgb=True, rgba=False): + super().__init__() + self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) if upsample else None + + # Uses stylegan1 style blocks for injecting structural latent. + self.conv0 = EqualConv2d(input_channels, filters, 3, padding=1) + self.to_noise0 = nn.Linear(1, filters) + self.noise0 = equal_lr(NoiseInjection(filters)) + self.adain0 = AdaptiveInstanceNorm(filters, latent_dim) + + self.to_style1 = nn.Linear(latent_dim, filters) + self.to_noise1 = nn.Linear(1, filters) + self.conv1 = Conv2DMod(filters, filters, 3) + + self.to_style2 = nn.Linear(latent_dim, filters) + self.to_noise2 = nn.Linear(1, filters) + self.conv2 = Conv2DMod(filters, filters, 3) + + self.activation = leaky_relu() + self.to_rgb = RGBBlock(latent_dim, filters, upsample_rgb, rgba) + + def forward(self, x, prev_rgb, istyle, inoise, structure_input): + if exists(self.upsample): + x = self.upsample(x) + + inoise = inoise[:, :x.shape[2], :x.shape[3], :] + noise0 = self.to_noise0(inoise).permute((0, 3, 1, 2)) + noise1 = self.to_noise1(inoise).permute((0, 3, 1, 2)) + noise2 = self.to_noise2(inoise).permute((0, 3, 1, 2)) + + structure = torch.nn.functional.interpolate(structure_input, size=x.shape[2:], mode="nearest") + x = self.conv0(x) + x = self.noise0(x, noise0) + x = self.adain0(x, structure) + + style1 = self.to_style1(istyle) + x = self.conv1(x, style1) + x = self.activation(x + noise1) + + style2 = self.to_style2(istyle) + x = self.conv2(x, style2) + x = self.activation(x + noise2) + + rgb = self.to_rgb(x, prev_rgb, istyle) + return x, rgb + + +class GeneratorBlock(nn.Module): + def __init__(self, latent_dim, input_channels, filters, upsample=True, upsample_rgb=True, rgba=False, structure_input=False): + super().__init__() + self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) if upsample else None + + self.structure_input = structure_input + if self.structure_input: + self.structure_conv = nn.Conv2d(3, input_channels, 3, padding=1) + input_channels = input_channels * 2 + + self.to_style1 = nn.Linear(latent_dim, input_channels) + self.to_noise1 = nn.Linear(1, filters) + self.conv1 = Conv2DMod(input_channels, filters, 3) + + self.to_style2 = nn.Linear(latent_dim, filters) + self.to_noise2 = nn.Linear(1, filters) + self.conv2 = Conv2DMod(filters, filters, 3) + + self.activation = leaky_relu() + self.to_rgb = RGBBlock(latent_dim, filters, upsample_rgb, rgba) + + def forward(self, x, prev_rgb, istyle, inoise, structure_input=None): + if exists(self.upsample): + x = self.upsample(x) + + if self.structure_input: + s = self.structure_conv(structure_input) + x = torch.cat([x, s], dim=1) + + inoise = inoise[:, :x.shape[2], :x.shape[3], :] + noise1 = self.to_noise1(inoise).permute((0, 3, 2, 1)) + noise2 = self.to_noise2(inoise).permute((0, 3, 2, 1)) + + style1 = self.to_style1(istyle) + x = self.conv1(x, style1) + x = self.activation(x + noise1) + + style2 = self.to_style2(istyle) + x = self.conv2(x, style2) + x = self.activation(x + noise2) + + rgb = self.to_rgb(x, prev_rgb, istyle) + return x, rgb + + +class Generator(nn.Module): + def __init__(self, image_size, latent_dim, network_capacity=16, transparent=False, attn_layers=[], no_const=False, + fmap_max=512, structure_input=False): + super().__init__() + self.image_size = image_size + self.latent_dim = latent_dim + self.num_layers = int(log2(image_size) - 1) + + filters = [network_capacity * (2 ** (i + 1)) for i in range(self.num_layers)][::-1] + + set_fmap_max = partial(min, fmap_max) + filters = list(map(set_fmap_max, filters)) + init_channels = filters[0] + filters = [init_channels, *filters] + + in_out_pairs = zip(filters[:-1], filters[1:]) + self.no_const = no_const + + if no_const: + self.to_initial_block = nn.ConvTranspose2d(latent_dim, init_channels, 4, 1, 0, bias=False) + else: + self.initial_block = nn.Parameter(torch.randn((1, init_channels, 4, 4))) + + self.initial_conv = nn.Conv2d(filters[0], filters[0], 3, padding=1) + self.blocks = nn.ModuleList([]) + self.attns = nn.ModuleList([]) + + for ind, (in_chan, out_chan) in enumerate(in_out_pairs): + not_first = ind != 0 + not_last = ind != (self.num_layers - 1) + num_layer = self.num_layers - ind + + attn_fn = attn_and_ff(in_chan) if num_layer in attn_layers else None + + self.attns.append(attn_fn) + + if structure_input: + block_fn = GeneratorBlockWithStructure + else: + block_fn = GeneratorBlock + + block = block_fn( + latent_dim, + in_chan, + out_chan, + upsample=not_first, + upsample_rgb=not_last, + rgba=transparent + ) + self.blocks.append(block) + + def forward(self, styles, input_noise, structure_input=None, starting_shape=None): + batch_size = styles.shape[0] + image_size = self.image_size + + if self.no_const: + avg_style = styles.mean(dim=1)[:, :, None, None] + x = self.to_initial_block(avg_style) + else: + x = self.initial_block.expand(batch_size, -1, -1, -1) + if starting_shape is not None: + x = F.interpolate(x, size=starting_shape, mode="bilinear") + + rgb = None + styles = styles.transpose(0, 1) + x = self.initial_conv(x) + + if structure_input is not None: + s = torch.nn.functional.interpolate(structure_input, size=x.shape[2:], mode="nearest") + for style, block, attn in zip(styles, self.blocks, self.attns): + if exists(attn): + x = checkpoint(attn, x) + if structure_input is not None: + if exists(block.upsample): + # In this case, the structural guidance is given by the extra information over the previous layer. + twoX = (x.shape[2]*2, x.shape[3]*2) + sn = torch.nn.functional.interpolate(structure_input, size=twoX, mode="nearest") + s_int = torch.nn.functional.interpolate(s, size=twoX, mode="bilinear") + s_diff = sn - s_int + else: + # This is the initial case - just feed in the base structure. + s_diff = s + else: + s_diff = None + x, rgb = checkpoint(block, x, rgb, style, input_noise, s_diff) + + return rgb + + +# Wrapper that combines style vectorizer with the actual generator. +class StyleGan2GeneratorWithLatent(nn.Module): + def __init__(self, image_size, latent_dim=512, style_depth=8, lr_mlp=.1, network_capacity=16, transparent=False, + attn_layers=[], no_const=False, fmap_max=512, structure_input=False): + super().__init__() + self.vectorizer = StyleVectorizer(latent_dim, style_depth, lr_mul=lr_mlp) + self.gen = Generator(image_size, latent_dim, network_capacity, transparent, attn_layers, no_const, fmap_max, + structure_input=structure_input) + self.mixed_prob = .9 + self._init_weights() + + def noise(self, n, latent_dim, device): + return torch.randn(n, latent_dim).cuda(device) + + def noise_list(self, n, layers, latent_dim, device): + return [(self.noise(n, latent_dim, device), layers)] + + def mixed_list(self, n, layers, latent_dim, device): + tt = int(torch.rand(()).numpy() * layers) + return self.noise_list(n, tt, latent_dim, device) + self.noise_list(n, layers - tt, latent_dim, device) + + def latent_to_w(self, style_vectorizer, latent_descr): + return [(style_vectorizer(z), num_layers) for z, num_layers in latent_descr] + + def styles_def_to_tensor(self, styles_def): + return torch.cat([t[:, None, :].expand(-1, n, -1) for t, n in styles_def], dim=1) + + # To use per the stylegan paper, input should be uniform noise. This gen takes it in as a normal "image" format: + # b,f,h,w. + def forward(self, x, structure_input=None, fit_starting_shape_to_structure=False): + b, f, h, w = x.shape + + full_random_latents = True + if full_random_latents: + style = self.noise(b*2, self.gen.latent_dim, x.device) + w = self.vectorizer(style) + # Randomly distribute styles across layers + w_styles = w[:,None,:].expand(-1, self.gen.num_layers, -1).clone() + for j in range(b): + cutoff = int(torch.rand(()).numpy() * self.gen.num_layers) + if cutoff == self.gen.num_layers or random() > self.mixed_prob: + w_styles[j] = w_styles[j*2] + else: + w_styles[j, :cutoff] = w_styles[j*2, :cutoff] + w_styles[j, cutoff:] = w_styles[j*2+1, cutoff:] + w_styles = w_styles[:b] + else: + get_latents_fn = self.mixed_list if random() < self.mixed_prob else self.noise_list + style = get_latents_fn(b, self.gen.num_layers, self.gen.latent_dim, device=x.device) + w_space = self.latent_to_w(self.vectorizer, style) + w_styles = self.styles_def_to_tensor(w_space) + + starting_shape = None + if fit_starting_shape_to_structure: + starting_shape = (x.shape[2] // 32, x.shape[3] // 32) + # The underlying model expects the noise as b,h,w,1. Make it so. + return self.gen(w_styles, x[:,0,:,:].unsqueeze(dim=3), structure_input, starting_shape), w_styles + + def _init_weights(self): + for m in self.modules(): + if type(m) in {nn.Conv2d, nn.Linear} and hasattr(m, 'weight'): + nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu') + + for block in self.gen.blocks: + nn.init.zeros_(block.to_noise1.weight) + nn.init.zeros_(block.to_noise2.weight) + nn.init.zeros_(block.to_noise1.bias) + nn.init.zeros_(block.to_noise2.bias) + + +class DiscriminatorBlock(nn.Module): + def __init__(self, input_channels, filters, downsample=True): + super().__init__() + self.filters = filters + self.conv_res = nn.Conv2d(input_channels, filters, 1, stride=(2 if downsample else 1)) + + self.net = nn.Sequential( + nn.Conv2d(input_channels, filters, 3, padding=1), + leaky_relu(), + nn.Conv2d(filters, filters, 3, padding=1), + leaky_relu() + ) + + self.downsample = nn.Sequential( + Blur(), + nn.Conv2d(filters, filters, 3, padding=1, stride=2) + ) if downsample else None + + def forward(self, x): + res = self.conv_res(x) + x = self.net(x) + if exists(self.downsample): + x = self.downsample(x) + x = (x + res) * (1 / math.sqrt(2)) + return x + + +class StyleGan2Discriminator(nn.Module): + def __init__(self, image_size, network_capacity=16, fq_layers=[], fq_dict_size=256, attn_layers=[], + transparent=False, fmap_max=512, input_filters=3, quantize=False, do_checkpointing=False, mlp=False): + super().__init__() + num_layers = int(log2(image_size) - 1) + + blocks = [] + filters = [input_filters] + [(64) * (2 ** i) for i in range(num_layers + 1)] + + set_fmap_max = partial(min, fmap_max) + filters = list(map(set_fmap_max, filters)) + chan_in_out = list(zip(filters[:-1], filters[1:])) + + blocks = [] + attn_blocks = [] + quantize_blocks = [] + + for ind, (in_chan, out_chan) in enumerate(chan_in_out): + num_layer = ind + 1 + is_not_last = ind != (len(chan_in_out) - 1) + + block = DiscriminatorBlock(in_chan, out_chan, downsample=is_not_last) + blocks.append(block) + + attn_fn = attn_and_ff(out_chan) if num_layer in attn_layers else None + + attn_blocks.append(attn_fn) + + if quantize: + quantize_fn = PermuteToFrom(VectorQuantize(out_chan, fq_dict_size)) if num_layer in fq_layers else None + quantize_blocks.append(quantize_fn) + else: + quantize_blocks.append(None) + + self.blocks = nn.ModuleList(blocks) + self.attn_blocks = nn.ModuleList(attn_blocks) + self.quantize_blocks = nn.ModuleList(quantize_blocks) + self.do_checkpointing = do_checkpointing + + chan_last = filters[-1] + latent_dim = 2 * 2 * chan_last + + self.final_conv = nn.Conv2d(chan_last, chan_last, 3, padding=1) + self.flatten = Flatten() + if mlp: + self.to_logit = nn.Sequential(nn.Linear(latent_dim, 100), + leaky_relu(), + nn.Linear(100, 1)) + else: + self.to_logit = nn.Linear(latent_dim, 1) + + self._init_weights() + + def forward(self, x): + b, *_ = x.shape + + quantize_loss = torch.zeros(1).to(x) + + for (block, attn_block, q_block) in zip(self.blocks, self.attn_blocks, self.quantize_blocks): + if self.do_checkpointing: + x = checkpoint(block, x) + else: + x = block(x) + + if exists(attn_block): + x = attn_block(x) + + if exists(q_block): + x, _, loss = q_block(x) + quantize_loss += loss + + x = self.final_conv(x) + x = self.flatten(x) + x = self.to_logit(x) + if exists(q_block): + return x.squeeze(), quantize_loss + else: + return x.squeeze() + + def _init_weights(self): + for m in self.modules(): + if type(m) in {nn.Conv2d, nn.Linear}: + nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu') + + # Configures the network as partially pre-trained. This means: + # 1) The top (high-resolution) `num_blocks` will have their weights re-initialized. + # 2) The haed (linear layers) will also have their weights re-initialized + # 3) All intermediate blocks will be frozen until step `frozen_until_step` + # These settings will be applied after the weights have been loaded (network_loaded()) + def configure_partial_training(self, bypass_blocks=0, num_blocks=2, frozen_until_step=0): + self.bypass_blocks = bypass_blocks + self.num_blocks = num_blocks + self.frozen_until_step = frozen_until_step + + # Called after the network weights are loaded. + def network_loaded(self): + if not hasattr(self, 'frozen_until_step'): + return + + if self.bypass_blocks > 0: + self.blocks = self.blocks[self.bypass_blocks:] + self.blocks[0] = DiscriminatorBlock(3, self.blocks[0].filters, downsample=True).to(next(self.parameters()).device) + + reset_blocks = [self.to_logit] + for i in range(self.num_blocks): + reset_blocks.append(self.blocks[i]) + for bl in reset_blocks: + for m in bl.modules(): + if type(m) in {nn.Conv2d, nn.Linear}: + nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu') + for p in m.parameters(recurse=True): + p._NEW_BLOCK = True + for p in self.parameters(): + if not hasattr(p, '_NEW_BLOCK'): + p.DO_NOT_TRAIN_UNTIL = self.frozen_until_step + + +class StyleGan2DivergenceLoss(L.ConfigurableLoss): + def __init__(self, opt, env): + super().__init__(opt, env) + self.real = opt['real'] + self.fake = opt['fake'] + self.discriminator = opt['discriminator'] + self.for_gen = opt['gen_loss'] + self.gp_frequency = opt['gradient_penalty_frequency'] + self.noise = opt['noise'] if 'noise' in opt.keys() else 0 + + def forward(self, net, state): + real_input = state[self.real] + fake_input = state[self.fake] + if self.noise != 0: + fake_input = fake_input + torch.rand_like(fake_input) * self.noise + real_input = real_input + torch.rand_like(real_input) * self.noise + + D = self.env['discriminators'][self.discriminator] + fake = D(fake_input) + if self.for_gen: + return fake.mean() + else: + real_input.requires_grad_() # <-- Needed to compute gradients on the input. + real = D(real_input) + divergence_loss = (F.relu(1 + real) + F.relu(1 - fake)).mean() + + # Apply gradient penalty. TODO: migrate this elsewhere. + if self.env['step'] % self.gp_frequency == 0: + gp = gradient_penalty(real_input, real) + self.metrics.append(("gradient_penalty", gp.clone().detach())) + divergence_loss = divergence_loss + gp + + real_input.requires_grad_(requires_grad=False) + return divergence_loss + + +class StyleGan2PathLengthLoss(L.ConfigurableLoss): + def __init__(self, opt, env): + super().__init__(opt, env) + self.w_styles = opt['w_styles'] + self.gen = opt['gen'] + self.pl_mean = None + self.pl_length_ma = EMA(.99) + + def forward(self, net, state): + w_styles = state[self.w_styles] + gen = state[self.gen] + pl_lengths = calc_pl_lengths(w_styles, gen) + avg_pl_length = np.mean(pl_lengths.detach().cpu().numpy()) + + if not is_empty(self.pl_mean): + pl_loss = ((pl_lengths - self.pl_mean) ** 2).mean() + if not torch.isnan(pl_loss): + return pl_loss + else: + print("Path length loss returned NaN!") + + self.pl_mean = self.pl_length_ma.update_average(self.pl_mean, avg_pl_length) + return 0 + + +@register_model +def register_stylegan2_lucidrains(opt_net, opt): + is_structured = opt_net['structured'] if 'structured' in opt_net.keys() else False + attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else [] + return StyleGan2GeneratorWithLatent(image_size=opt_net['image_size'], latent_dim=opt_net['latent_dim'], + style_depth=opt_net['style_depth'], structure_input=is_structured, + attn_layers=attn) + + +@register_model +def register_stylegan2_discriminator(opt_net, opt): + attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else [] + disc = StyleGan2Discriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'], attn_layers=attn, + do_checkpointing=opt_get(opt_net, ['do_checkpointing'], False), + quantize=opt_get(opt_net, ['quantize'], False), + mlp=opt_get(opt_net, ['mlp_head'], True)) + if 'use_partial_pretrained' in opt_net.keys(): + disc.configure_partial_training(opt_net['bypass_blocks'], opt_net['partial_training_blocks'], opt_net['intermediate_blocks_frozen_until']) + return StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability'])